Graph Deep Learning with Absent Data.
To handle incomplete graph data, one popular way commonly adopted by existing algorithms is data imputation technique, such as matrix completion and generative adversarial learning. For matrix completion, sRGCNN [1]}, GC-MC [2]}, and NMTR [3]} formulate the user-item rating matrix, users (or items), and the observed ratings as bipartite graph, nodes, and links, respectively. Then they apply a graph auto-encoder (GAE) to predict the absent linkages between node pairs.
By introducing an additional adversarial loss, GINN [4]} trains a graph denoising auto-encoder to build intermediate representations of all nodes with the help of a pre-processing graph for data imputation.
To further improve the quality of attribute restoration, recent efforts combine the processes of data imputation and representation learning into a united framework. For instance, GRAPE [5]} formulates the feature imputation as an edge-level prediction task on the graph. After that, GCNMF [6]} estimates the incomplete variables by enforcing them to follow the Gaussian mixture distribution using a Gaussian mixture model (GMM).
